A review is a critical evaluation of a publication (e.g., books, movies, etc.), product (e.g., automobiles, electronics, kitchen ware, etc.), service (e.g., hotels, restaurants, etc.), or company, or an event or performance (e.g., concerts, theatrical shows, etc.). In addition to a critical evaluation, the review's author may choose to give a quantitative rating (e.g., 3 stars out of 5) to indicate the relative merits of the thing being reviewed as compared to its peers. Websites such as Google, Amazon, TripAdvisor, etc. provide the best platforms for people to share their critical reviews.
There are several entities involved in a review hosting system. The reviewers who review a hotel, the users who read reviews and make purchase decision, the product or service that are reviewed and the hosting sites that hosts those reviews. Everyone's interest is best served when there reviews are fair and truthful.
Online reviews provide an unprecedented mechanism for customers to inform potential customers. On the other hand, reviews can suffer from: (a) relevance, as the needs and expectations of customers varies, and (b) fraudulent behavior from “biased” reviewers or the product providers themselves. As a result, it is not easy for customers to interpret and use such reviews. One often relies either on the overall or cumulative rating or laboriously goes through many reviews to identify relevance and trustworthiness.
Most review sites give visitors a cumulative (e.g., average star rating), and sometimes categorized (e.g., reviews of different aspects of a product or service. The problem with cumulative rating is that the average may not be a true reflection of the opinions of the reviewers. Furthermore, online reviews may be burdensome when the product or service appears on more than one review site.
Currently, the existence of multiple review sites can add to the confusion of the user. Each of these sites may give a different view of a product or service, and it can be difficult for the consumer to know whom to trust.
Previous efforts have focused on identifying fraudulent reviews and reviewers using a diverse set of review data in both supervised and unsupervised manners, but only consider a single review hosting site. Certain methodologies detect fraud by exploiting network effects and clique structures among reviewers and products to identify fraud. Text-based detection of fraud is studied to spot a fake review without having the context of the reviewer and reviewed product. Temporal patterns, such as bursts, have also been identified as fraudulent behavior. Various other types of methodologies detect fraud using unusual behavioral footprints, unusual distributional footprints, unexpected rules and unusual rating behaviors.
Thus, there is a need for leveraging reviews from multiple review sites to make more informed decisions and detect fraud. The invention satisfies this need.